Program Tools for Estimation of the Green Software Reliability

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1 Program Tools for Estimation of the Green Software Reliability Dmitry Maesky *, Elena Maeskaya, Ludmila Shapa and Dmitry Stetsyuk Odessa National Polytechnic Uniersity, Bouleard of Shechenko, 1, Ukraine Abstract. The paper deals with a reiew of the current software of estimation of the green software reliability. The paper demonstrates that any software is capable of becoming a green one if it is at the high reliability leel, which has to be continuously measured and controlled. In order to implement these actions, the paper offers the results of reiewing the most frequently used software performing the reliability estimation. On the basis of the analysis of this software characteristics a taxonomic system of classification features describing its (software) functionality and the ease of presentation of the estimation results with the help of this software is offered. By using the offered taxonomy a system of classification of software reliability estimation tools was created. The conclusions of the current trends of research in the area of reliability estimation and green software deelopment were made. 1 Introduction At present any software is considered to be a green one if the electric energy is saed in using this type of software. It is really so. Howeer as to the criticalpurpose software systems the saing factor is just one (and not the most important) of the requirements of greenness. On the first place in the critical-purpose software systems there is the factor of reliability and resilience. Indeed, the defects containing in a software can destabilize the performance of many critical infrastructures such as nuclear power, transport, communications, health serice. Herein the material and energy resources required to reconstruct the mentioned critical infrastructures or to eliminate the consequences of accidents occurring at them can be many times greater than the cost of electrical energy, which is saed in the process of their usage. So, the proision of stated indicators of Green Software reliability is of ital importance for creating the qualitatie and safe software systems. At present there exist about sixty arious software reliability grows models (SRGM) to calculate these alues. To obtain the numerical alues of the reliability indicators these models uses time series of data of the identified defects. The indicators themseles are found by doing the complicated math and using the nonlinear programming unit. The manual calculation of these coefficients takes a lot of time, and this fact became an impulsie cause for creation of a large number (more than twenty) of program tools for software reliability estimation. Each of these software tools as a rule permits for estimating the reliability according to not one but a significant number of SPMs. The aailability of the ariety of program tools makes some difficulties for a deeloper in choosing the best and most suitable SRGM of the amount of implemented in it (program tool). The complexity of the task is aggraated by the two factors. Firstly, as a rule the software reliability theory is unknown to deelopers. Secondly, there is no single all-purpose SRGM, which accurately determines the reliability indicators in all cases. In choosing the model the indicators classification system offered for the first time in [1] and described in detail in monograph [2] can be useful for deelopers. Howeer, the classification of software reliability estimation program tools is absent at the moment. That is why its deelopment is timely and topical. 2 The reiew of reliability estimation program tools The reiew of program tools found in the aailable literary sources containing the information, the amount of which allows to understand their functionality and user interface, is gien below. SRMP (Software Reliability Modeling Programs) [3] a program tool for reliability estimation created by Be Littlewood in It includes the following reliability models: Goel-Okumoto, Musa-Okumoto, Duane as well as Littlewood-Werall quadratic and linear models. SRMP presents the results of estimation in the form of tables and graphs. The estimation accuracy indicators are formed in a separate way. SRMP accepts only time between failure as input data. The results proided in graphic form with some statistics. SMERF [3, 4] (Statistical Modelling and Estimation of Reliability Functions for Software) a tool deeloped by William Farr in The reliability estimation according to such SRMs as Littlewood-Werall quadratic and linear models, Musa s basic and logarithmic models, geometric one, Jelinsky-Moranda, Yamada, Schneidewind, Brooks and Motley s binomial and * Corresponding author: Dmitry.A.Maesky@gmail.com The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creatie Commons Attribution License 4.0 (

2 Poisson s models, generalized Poisson model is implemented in it. RGA (Reliability Growth Analysis and Repairable System Analysis) a program tool created in 1992 by ReliaSoft Corporation and Dr. Larry Crow. According to [5], RGA includes full support for traditional reliability growth analysis using the applicable models Crow- AMSAA (NHPP), Duane, Gompertz, Modified Gompertz, Lloyd-Lipow or Logistic for a ariety of deelopmental data types time-to-failure, discrete (success/failure) and reliability data. As described in [5], RGA offers all of the major reliability growth models, plus adanced analysis methods that are not aailable in another program tools. RGA also proides opportunities for fielded repairable system analysis, including a Design of Reliability Test utility and a method for analyzing the system's reliability behaiour oer time in order to calculate optimum oerhaul times and other metrics of interest. The latest ersion of the program tool was published in March CASRE (Computer-Aided Software Reliability Estimation Tool) [3, 6] a tool, which was implemented in 1993 by Allen P. Nikora. It includes the following reliability models: Brooks-Motley binominal model, Schneidewind, Jelinsky-Moranda, Littlewood-Verrall, Musa-Okumoto, basic and logarithmic Musa models. CASRE proides the deeloped automated support of software reliability modelling. The main adantage of CASRE is its arious reliability estimations under a paradigm which linearly combines the component models. This tool features an enhanced graphical userinterface which greatly facilitates the process of software reliability estimation. ROBUST (Reliability of Basic and Ultra-reliable Software systems) [7, 8] a program tool created by Y. K. Malaiya, Jason Denton and Naixin Li in It supports four SRGMs: Muas, Musa-Okumoto, Duane and S-shaped. It gies the possibility to take into account the metrics containing the information of a program, crew of deelopers, etc. ROBUST proides a wide range of support for traditional software reliability growth models. It is capable of working with failure data in either a cumulatie time to failure or failure interal format. CARATS (Computer-Aided Reliability Assessment Tool for Software Based on Object-Oriented Design) [9] a tool created by Chien-Chia Chen, Chu-Ti Lin, Hen- Hsen Huang and Shih-Wei Huang. The following SRGMs are implemented: Yamada, Musa-Okumoto, Goel-Okumoto, Duane. There is a chance to compare SRGMs with the help of maximum likelihood method and the one of least deiation. CARATS can use both traditional SRGMs and neural-network methods to assess software reliability. Refis (A New Statistical Software Reliability Tool) [10] a program tool deeloped by M. A. A. Boon, E. Brandt, Corro Ramos, A. Di Bucchianico and R. Henzen in It supports the following SRGMs: Littlewood- Verall, geometrical one, Musa, Jelinsky-Moranda, Muas- Okumoto, Goel-Okumoto, Schick and Wolerton, Schneidewind, S-shaped, Duane. SRATS (Software Reliability Assessment Tool on Spreadsheet) [11] a tool, which was created in SRATS functions as a Microsoft Excel add-in, that is why it cannot be used as an independent tool. The latest ersion came into existence in SafeMan [12] a program tool deeloped by Takaji Fujiwara and Mitsuhiro Kimura in Only two RSGMs hae been implemented Musa model and S- shaped model. This tool has function of automatic selection of the optimum SRGM. 3 Classification features formation The presented classification features aimed at the software reliability estimation by program tools are to reflect two factors, which are important for users: the ease of a program tool usage and its functionality. The first factor is stipulated by the interface characteristics of a program tool, and the second by mathematical apparatus implemented in it. The authors offer to attribute the method of source data statement and the one of result presentation to the interface features. Let us consider them in detail. The ease (and frequently the use) is identified with the help of a method of source data statement. E.g. if a user has got the cumulatie series of distribution of the failures detected in the course of time, and a program tool is able only to fix the moments of their detection at the input then such a program tool cannot be used. That is why the author offer to use a type of source data, which is appropriate for this particular program tool, as the first classification feature. We distinguish such types of source data presentation: TF (Time of Failure) the astronomical time of the failure emergence; TBF (Time Between Failures) time interal between two successie failures; CF (Cumulatie Failure) the cumulatie amount of failures; FI (Failures experienced in a time Interal) a number of failures detected at the predetermined time interal. The second feature of Software reliability estimation tools classification is a method of results presentation of their run. The method of presentation is of great importance since it is the method itself that determines the ease for a user, who deal with a Software tool. The coefficients of applied reliability models do not proide a user with any useful information. On the basis of models coefficients, the main reliability indicators - failure rate, MTBF and hazard function - can be only identified. These indicators proide the users, who are not informed enough in the theory of Software reliability, with little data as well. The users of the Software reliability estimation tools are to be either the specialists in the program debug area or the ones in the Software engineering field. For them the results of run of Software reliability estimation tools should be presented in the familiar terms a number of failures at a time and the law of changes of the number of failures oer time. As the analysis carried out by the authors has 2

shown the majority of the current Software reliability estimation tools proides the user with the two mentioned types of results the main indicators of reliability and the ones of the number of

The first type is a table in which the times and the number of failures in Software oer times are gien. The second type are the graphs displaying the data of the table.

3 shown the majority of the current Software reliability estimation tools proides the user with the two mentioned types of results the main indicators of reliability and the ones of the number of failures (Fig. 1). Fig. 1. The results of program RGA run There are two types of presentation of the indicators of the number of failures. The first type is a table in which the times and the number of failures in Software oer times are gien. The second type are the graphs displaying the data of the table. The graphic presentation of results is more demonstratie but the tabular form is more functional. Using the table data one can accurately identify the number of failures in software and ealuate the rate of its changes. We distinguish three features demonstrating the presentation of the results of Software tools run: R the results are the indicators of reliability; T the results are presented in a tabular form; G the results are gien in a graphical form. In each case the simultaneous usage of seeral features is possible. The third classification feature displays the functionality of Software reliability estimation tools. At present the functionality is indicated on the basis of the SRGM list implemented in this model. Howeer, a mere listing of models cannot be informatie for a user in principle. The users of Software reliability estimation tools are not specialized in the theory of Software reliability. They do not know the peculiarities of existing models and the rules of their usage. The ideal case is when a user does not generally suspect about the existence of the models. In this case the model itself is not of any importance for him (her) to obtain some result. The only thing a user need is to obtain a reliable result. Howeer, all the considered Software reliability estimation tools are implied to be utilized by a user for the manual choice of one or seeral models from the list (Fig. 2). Fig. 2. The window of choice of the reliability model in program SMERFS Taking into account the fact that the different set of models are implemented in different Software reliability estimation tools the choice of the necessary tool is the impossible task for an unexperienced user today. This task can be carried out only with the help of a specialist in this field or at random. In the gien paper the authors offer to change the approach to the choice of Software reliability estimation tool. The users are not informed in reliability models area but they know the peculiarities of their own Software, the conditions of its deelopment and testing. Howeer, each of the reliability models is created taking account the definite assumptions, which are generally determined by testing conditions. E.g. in the majority of models an assumption is taken that in correcting a failure the new failures are not included in Software. That is why the choice of a particular reliability model in eery particular case is made on the basis of comparison of assumptions of a model and the conditions of testing. On the other hand, a set of models implemented in each Software reliability estimation tool, creates a set of assumptions, with the help of which it meets this Software tool. So it becomes possible to choose a Software tool on the basis of comparing the testing conditions and a set of assumptions. In the fullest form the assumptions of the majority of RSGMs hae been analysed and generalized in the paper [13]. In this paper the authors offer a matrix of RSGM assumptions in the lines of which all models assumptions are grouped and in the columns the models, which meet these assumptions. All assumptions are united in the four groups: I take into account the way of detecting and correcting the failures; II display the models classification according to [1]; III take into account the way of distributing the detected failures in time; IV the assumptions, which are inherent only to some indiidual models. Each of the groups in its turn is diided into subgroups. The complete list of all subgroups with the detailed explanations one can find in the aboe mentioned paper. The matrix of matches between assumptions and reliability models is presented ibidem. Based on all said aboe as the third functional feature of Software reliability estimation tools the authors offer to use the assumptions of models implemented in them (tools) according to the matrix of assumptions. The authors of article [13] propose the following sequence of steps for software reliability ealuation using assumption matrix: 1. Analysis of software requirements and software engineering process. 2. Obtaining of the information about assumptions usable to the deeloped software. 3. SRGM choice using assumption matrix. 4. Determination of SRGM input parameters and analysis of their complexing opportunities. 5. SRGM parameters fitting. 3

4 6. Calculation of software reliability measures and statistical analysis of obtained data. Fig. 3 shows a fragment of the assumptions matrix (according to [13], Table 1). Fig. 3. Fragment of Assumption Matrix The forth classification feature, which also indicates a Software tool functionality, is to perform the prediction of reliability indicators changes in the subsequent periods of time. Broadly speaking the reliability indicators prediction is not a complicated mathematical problem. The point is that a reliability model is finally a mathematical formula determining the law of changes of the number of failures oer time. The formula contains the coefficients identified on the basis of the time series of failure detection. That is why one should indicate the model coefficients only once and then, substituting different time alues into the formula, identify the predicted indicators of reliability. Howeer, taking into account the fact that a final user is badly (or not at all) informed in the reliability models area a Software tool is to take the task of calculation of the predicted indicators. We are able to distinguish two alues of the feature: E a Software tool performs only the reliability estimation; P a Software tool performs both the estimation and prediction of reliability. We can present the classification features (taxons) system graphically (Fig. 4). The group of interface features determines two aspects in the system. First, it can be called a traditional ease when using a Software tool. Groups of Classification Features Interface features Functional Features On Source Data On the method of Presentation On Assumptions of Models On the Output Time of Failure (TF) Reliability Estimations (R) Group I Parameters of Estimations (E) Time between Failure (TBF) Table (T) Group II Parameters of Prediction (P) Cumulatie Number of Failure (CF) Graph (G) Group III The Number of Failures in Time Int. (FI) Group IV Fig. 4. Classification Features of Software Reliability Estimations Tools Howeer, the way of setting the source data, which can be also attributed to the source data, which can be also attributed to the interface features, determines finally a reliability model used That is why we can say that in the deeloped system of classification the interface features determine one more methodological aspect of usage of Software reliability estimation tools. The group of functional features identifies the potential of a Software tool to estimate the reliability. The usage of simple assumptions of models understandable for a user allows to significantly ease the choice of a suitable Software tool of all the diersity of the current Software tools. On the basis of the offered classification features the classification of Software reliability estimation tools described aboe has been carried out. The classification is gien in table 1 at the preious page. The distinguished features hae been presented in the columns of the table according to the hierarchy leels shown in Fig.4. 4

5 Software Tool Table 1. Classification of Software Reliability Estimation Tools Interface Features Functional Features Source Data Results Assumptions of Models Prediction TF TBF C FI G T I II III IV E P SRMP I 1, 2.1, 3 2, 5 2.1, 5.2 SMERFS I II 1, 2, 4, 5, 6, 7 1.1, 2.1, 2.3, 4.1, RGA I 1.2, 2.1, CASRE I 1, 2, 3.1 1, 2, 7 1.1, 2.1, 7.1 ROBUST I 1, 2.2, 3 2, 4, 5, 7 2.3, 4.1, CARATS I 1, 2.2, 3 2, 4, 5, 7 2.1, 4.1, 1.1, 2, Refis I II 1, 2, 4, 5, 6, 7 4.1, 5, 6.1, 7.1 SRATS I 1, 2.1,3 2, 4, 7 2.1, 4.1, 7.1 SafeMan I 1.1, 2.2, 3 2, 4 2.3, 4.1 Conclusion The paper offers an hierarchic group of classification features taxons, which represent the Software reliability estimation tools and their main characteristics in the most complete and correct way. Both interface and functional (performance) characteristics of such kind of Software tools hae been clearly highlighted in the system. The usage of the deeloped taxonometric features allows to create a classification of the current Software tools. The deeloped classification system is able to facilitate a user s efforts in choosing a software reliability estimation tool necessary for him (her) among the ariety of tools. The adantage of the classification system offered is as follows: it can be employed by users, who are not specialists in the field of Software reliability theory. An ordinary user does not know and he (she) must not, which reliability models are realized in this or that software tool, and understand the peculiarities of these models operation. He (she) does not need to know such terrible specific terms as Вeйбулл s distribution, «Software Reliability Growth Models», «Maximum Likelihood Estimation» and others. Furthermore a user is een unaware of the existence of Software reliability models itself. On the basis of the system offered a user is able to make a choice of the most suitable Software tool, haing only understandable information aailable for him (her). The information only reflects the conditions of Software testing and creating as well as a defect detection and correction method. The usage of the reliability estimation software tool classification demonstrated in the article will allow to make the reliability theory closer to eeryday life and gie an opportunity to specialists to deelop more reliable and stable Software.. References 1. J.D. Musa, K. Okumoto, Electronic Systems Effectieness and Life Cycle Costing, NATO ASI Series, 3, 395, (1983) 2. M.R. Lyu Handbook of Software Reliability Engineer-ing (New-York: McGraw-Hill, 1996) 3. G.E. Stark International Symposium on Software Reliability Engineering, 90, (1991) 4. D.R. Wallace, 26th Annual NASA Software Engineering Workshop, 147, (2001) 5. L. Crow, ReliaSoft, ( 6. M.R. Lyu, A.P. Nikora, Fifth International Workshop on Computer-Aided Software Engineering, 264, (1992) 7. Y. Malaiya, J. Denton, N. Li, ( ) 8. Li, N. Sixth International Symposium on Software Reliability Engineering, 375, (1995) 9. C.C. Chen, C.T. Lin, H.H. Huang, S.W. Huang, C.Y. Huang, IEEE Region 10 Conference TENCON 2006, 1, (2006) 10. M.A.A. Boon, E. Brandt, I.C. Ramos, ( 11. H. Okamura, T. Dohi, Software Reliability Engineering (ISSRE), 100, (2013) 12. T. Fujiwara, M. Kimur, JSEA, 7, 396, (2014) 13. V.S. Kharchenko, O.M. Tarasyuk, V.V. Sklyar, V.Y. Dubnitsky, International Computer Software and Applications (COMPSAC 2002), 541, (2002) 5

A New Analysis Concept in Applying Software Reliability Growth Models and Tool Implementation: The SafeMan

A New Analysis Concept in Applying Software Reliability Growth Models and Tool Implementation: The SafeMan Journal of Software Engineering and Applications, 2014, 7, 396-405 Published Online May 2014 in SciRes. http://www.scirp.org/journal/jsea http://dx.doi.org/10.4236/jsea.2014.75036 A New Analysis Concept